
Model-based optical coherence tomography angiography enables motion-insensitive vascular imaging
Author(s) -
Wei Wei,
Andrea Cogliati,
Cristina Canavesi
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.420091
Subject(s) - optical coherence tomography , coherence (philosophical gambling strategy) , computer vision , artificial intelligence , biomedical engineering , scanner , computer science , visualization , image resolution , angiography , temporal resolution , optics , materials science , physics , radiology , medicine , quantum mechanics
We present a significant step toward ultrahigh-resolution, motion-insensitive characterization of vascular dynamics. Optical coherence tomography angiography (OCTA) is an invaluable diagnostic technology for non-invasive, label-free vascular imaging in vivo . However, since it relies on detecting moving cells from consecutive scans, high-resolution OCTA is susceptible to tissue motion, which imposes challenges in resolving and quantifying small vessels. We developed a novel OCTA technique named ultrahigh-resolution factor angiography (URFA) by modeling repeated scans as generative latent variables, with a common variance representing shared features and a unique variance representing motion. By iteratively maximizing the combined log-likelihood probability of these variances, the unique variance is largely separated. Meanwhile, features in the common variance are decoupled, in which vessels with dynamic flow are extracted from tissue structure by integrating high-order factors. Combined with Gabor-domain optical coherence microscopy, URFA successfully extracted high-resolution cutaneous vasculature despite severe involuntary tissue motion and scanner oscillation, significantly improving the visualization and characterization of micro-capillaries in vivo . Compared with the conventional approach, URFA reduces motion artifacts by nearly 50% on average, evaluated on local differences.